There is mounting evidence that recombination events are not randomly distributed in the human genome, but tend cluster into distinct regions, so-called recombination hotspots. My PhD work was ...focussed on developing an understanding of how meiotic recombination events are distributed in the human Major Histocompatibility (MHC) Class II region. To this end, a large number of SNPs in this region was identified and genotyped, and high-resolution linkage disequilibrium (LD) patterns were examined for evidence of historical recombination. Three regions of LD breakdown, i.e. putative recombination hotspots were localised. For these regions, allele-specific PCR methods were used to selectively amplify recombinant molecules directly from sperm DNA. This lead to the identification of three novel crossover hotspots, within which I was able to demonstrate an extremely localised nature of crossover breakpoints. Furthermore, molecular characterisation of the three hotspots showed that crossover rates can vary dramatically from one hotspot to the next and that LD patterns cannot be used to readily predict these rates. I also investigated the relative importance of known recombination hotspots vs. population history in shaping LD in three human populations (UK North Europeans, Saami and Zimbabweans). At least in this segment of the MHC, haplotype structures directly relate to fine-scale patterns of meiotic recombination, even though a distinct paucity of "universal" haplotypes (haplotypes shared by all three populations) was observed.
There is mounting evidence that recombination events are not randomly distributed in the human genome, but tend cluster into distinct regions, so-called recombination hotspots. My PhD work was ...focussed on developing an understanding of how meiotic recombination events are distributed in the human Major Histocompatibility (MHC) Class II region. To this end, a large number of SNPs in this region was identified and genotyped, and high-resolution linkage disequilibrium (LD) patterns were examined for evidence of historical recombination. Three regions of LD breakdown, i.e. putative recombination hotspots were localised. For these regions, allele-specific PCR methods were used to selectively amplify recombinant molecules directly from sperm DNA. This lead to the identification of three novel crossover hotspots, within which I was able to demonstrate an extremely localised nature of crossover breakpoints. Furthermore, molecular characterisation of the three hotspots showed that crossover rates can vary dramatically from one hotspot to the next and that LD patterns cannot be used to readily predict these rates. I also investigated the relative importance of known recombination hotspots vs. population history in shaping LD in three human populations (UK North Europeans, Saami and Zimbabweans). At least in this segment of the MHC, haplotype structures directly relate to fine-scale patterns of meiotic recombination, even though a distinct paucity of “universal” haplotypes (haplotypes shared by all three populations) was observed.
Dogs process faces and emotional expressions much like humans, but the time windows important for face processing in dogs are largely unknown. By combining our non-invasive electroencephalography ...(EEG) protocol on dogs with machine-learning algorithms, we show category-specific dog brain responses to pictures of human and dog facial expressions, objects, and phase-scrambled faces. We trained a support vector machine classifier with spatiotemporal EEG data to discriminate between responses to pairs of images. The classification accuracy was highest for humans or dogs vs. scrambled images, with most informative time intervals of 100-140 ms and 240-280 ms. We also detected a response sensitive to threatening dog faces at 30-40 ms; generally, responses differentiating emotional expressions were found at 130-170 ms, and differentiation of faces from objects occurred at 120-130 ms. The cortical sources underlying the highest-amplitude EEG signals were localized to the dog visual cortex.
A subset of adult-onset asthma patients attribute their symptoms to damp and moldy buildings. Symptoms of idiopathic environmental intolerance (IEI) may resemble asthma and these two entities ...overlap. We aimed to evaluate if a distinct clinical subtype of asthma related to damp and moldy buildings can be identified, to unravel its corresponding pathomechanistic gene signatures, and to investigate potential molecular similarities with IEI. Fifty female adult-onset asthma patients were categorized based on exposure to building dampness and molds during disease initiation. IEI patients (n = 17) and healthy subjects (n = 21) were also included yielding 88 study subjects. IEI was scored with the Quick Environmental Exposure and Sensitivity Inventory (QEESI) questionnaire. Inflammation was evaluated by blood cell type profiling and cytokine measurements. Disease mechanisms were investigated via gene set variation analysis of RNA from nasal biopsies and peripheral blood mononuclear cells. Nasal biopsy gene expression and plasma cytokine profiles suggested airway and systemic inflammation in asthma without exposure to dampness (AND). Similar evidence of inflammation was absent in patients with dampness-and-mold-related asthma (AAD). Gene expression signatures revealed a greater degree of similarity between IEI and dampness-related asthma than between IEI patients and asthma not associated to dampness and mold. Blood cell transcriptome of IEI subjects showed strong suppression of immune cell activation, migration, and movement. QEESI scores correlated to blood cell gene expression of all study subjects. Transcriptomic analysis revealed clear pathomechanisms for AND but not AAD patients. Furthermore, we found a distinct molecular pathological profile in nasal and blood immune cells of IEI subjects, including several differentially expressed genes that were also identified in AAD samples, suggesting IEI-type mechanisms.
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